{"id":"https://openalex.org/W7147397192","doi":"https://doi.org/10.48550/arxiv.2603.28090","title":"To View Transform or Not to View Transform: NeRF-based Pre-training Perspective","display_name":"To View Transform or Not to View Transform: NeRF-based Pre-training Perspective","publication_year":2026,"publication_date":"2026-03-30","ids":{"openalex":"https://openalex.org/W7147397192","doi":"https://doi.org/10.48550/arxiv.2603.28090"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.28090","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28090","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.28090","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122665831","display_name":"Hyeonjun Jeong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeong, Hyeonjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011644446","display_name":"Juyeb Shin","orcid":"https://orcid.org/0009-0002-5304-4253"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shin, Juyeb","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5091555350","display_name":"Dongsuk Kum","orcid":"https://orcid.org/0000-0002-2590-4845"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kum, Dongsuk","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.6134999990463257,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.6134999990463257,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.06419999897480011,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.05869999900460243,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.7509999871253967},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6912999749183655},{"id":"https://openalex.org/keywords/radiance","display_name":"Radiance","score":0.6304000020027161},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5194000005722046},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.5041999816894531},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.49619999527931213},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.4672999978065491},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3610999882221222},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.3296999931335449}],"concepts":[{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.7509999871253967},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6912999749183655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6359000205993652},{"id":"https://openalex.org/C23690007","wikidata":"https://www.wikidata.org/wiki/Q1411145","display_name":"Radiance","level":2,"score":0.6304000020027161},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6121000051498413},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5856999754905701},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5194000005722046},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.5041999816894531},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.49619999527931213},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4672999978065491},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C2777021972","wikidata":"https://www.wikidata.org/wiki/Q22976830","display_name":"Uniqueness","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3059999942779541},{"id":"https://openalex.org/C2779627259","wikidata":"https://www.wikidata.org/wiki/Q779763","display_name":"Pretext","level":3,"score":0.30550000071525574},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C109950114","wikidata":"https://www.wikidata.org/wiki/Q4464732","display_name":"3D reconstruction","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C2776010242","wikidata":"https://www.wikidata.org/wiki/Q4677575","display_name":"Active perception","level":3,"score":0.2728999853134155},{"id":"https://openalex.org/C56435381","wikidata":"https://www.wikidata.org/wiki/Q1196371","display_name":"Geometric transformation","level":3,"score":0.27079999446868896},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.259799987077713},{"id":"https://openalex.org/C73313986","wikidata":"https://www.wikidata.org/wiki/Q355386","display_name":"Luminance","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.28090","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28090","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.28090","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28090","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Neural":[0],"radiance":[1,65],"fields":[2,66],"(NeRFs)":[3],"have":[4,37],"emerged":[5],"as":[6,85],"a":[7,23,79,123],"prominent":[8],"pre-training":[9,102],"paradigm":[10],"for":[11,101,167],"vision-centric":[12],"autonomous":[13],"driving,":[14],"which":[15],"enhances":[16],"3D":[17,32,89,94,114,127,133,159],"geometry":[18],"and":[19,61,69,87,135,162,171],"appearance":[20],"understanding":[21],"in":[22,109],"fully":[24],"self-supervised":[25],"manner.":[26],"To":[27],"apply":[28],"NeRF-based":[29],"pretraining":[30],"to":[31,41,164],"perception":[33],"models,":[34],"recent":[35],"approaches":[36],"simply":[38],"applied":[39],"NeRFs":[40,50],"volumetric":[42],"features":[43],"obtained":[44],"from":[45,141],"view":[46,52,57,142],"transformation.":[47,143],"However,":[48],"coupling":[49],"with":[51],"transformation":[53,58],"inherits":[54],"conflicting":[55],"priors;":[56],"imposes":[59],"discrete":[60],"rigid":[62],"representations,":[63],"whereas":[64],"assume":[67],"continuous":[68,132,158],"adaptive":[70],"functions.":[71],"When":[72],"these":[73],"opposing":[74],"assumptions":[75],"are":[76],"forced":[77],"into":[78],"single":[80],"pipeline,":[81],"the":[82,98,138,146,152,155],"misalignment":[83],"surfaces":[84],"blurry":[86],"ambiguous":[88],"representations":[90,115],"that":[91,129,179],"ultimately":[92],"limit":[93],"scene":[95,169,192],"understanding.":[96],"Moreover,":[97],"NeRF":[99,148],"network":[100,149],"is":[103],"discarded":[104],"during":[105],"downstream":[106,197],"tasks,":[107,153],"resulting":[108],"inefficient":[110],"utilization":[111],"of":[112,151,157],"enhanced":[113],"through":[116],"NeRF.":[117],"In":[118],"this":[119],"paper,":[120],"we":[121],"propose":[122],"novel":[124],"NeRF-Resembled":[125],"Point-based":[126],"detector":[128],"can":[130],"learn":[131],"representation":[134,160],"thus":[136],"avoid":[137],"misaligned":[139],"priors":[140],"NeRP3D":[144],"preserves":[145],"pre-trained":[147],"regardless":[150],"inheriting":[154],"principle":[156],"learning":[161],"leading":[163],"greater":[165],"potentials":[166],"both":[168],"reconstruction":[170,193],"detection":[172,198],"tasks.":[173,199],"Experiments":[174],"on":[175],"nuScenes":[176],"dataset":[177],"demonstrate":[178],"our":[180],"proposed":[181],"approach":[182],"significantly":[183],"improves":[184],"previous":[185],"state-of-the-art":[186],"methods,":[187],"outperforming":[188],"not":[189],"only":[190],"pretext":[191],"tasks":[194],"but":[195],"also":[196]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}